Titre :	IFRS 9 and CECL Credit Risk Modelling and Validation : A Practical Guide with Examples Worked in R and SAS
Type de document :	texte imprimé
Auteurs :	Tiziano Bellini, Auteur
Editeur :	Academic Press
Année de publication :	2019
Autre Editeur :	Elsevier
Importance :	298 p.
Présentation :	ill., Fig, Graph.
Format :	24 cm.
ISBN/ISSN/EAN :	978-0-12-814940-9
Note générale :	References 293 p. Index.
Langues :	Anglais
Catégories :	6 Politique, droit et économie:6.75 Administration et gestion:Opération de gestion:Gestion de risques
Mots-clés :	Credit risk  Pratical guide  IFRS  Risk management  Competing risk modelling
Index. décimale :	658.155
Résumé :	IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.

IFRS 9 and CECL Credit Risk Modelling and Validation : A Practical Guide with Examples Worked in R and SAS [texte imprimé] / Tiziano Bellini, Auteur . - London : Academic Press : London : Elsevier, 2019 . - 298 p. : ill., Fig, Graph. ; 24 cm.
ISBN : 978-0-12-814940-9
References 293 p.
Index.
Langues : Anglais

Catégories :	6 Politique, droit et économie:6.75 Administration et gestion:Opération de gestion:Gestion de risques
Mots-clés :	Credit risk  Pratical guide  IFRS  Risk management  Competing risk modelling
Index. décimale :	658.155
Résumé :	IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.